Parameter identification for fractional Ornstein–Uhlenbeck processes based on discrete observation-Reference-Cited by-同舟云学术

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Parameter identification for fractional Ornstein–Uhlenbeck processes based on discrete observation

Published:2014-01 Issue: Volume:36 Page:198-203
ISSN:0264-9993
Container-title:Economic Modelling
language:en
Short-container-title:Economic Modelling

Author:

Zhang Pu,Xiao Wei-lin,Zhang Xi-li,Niu Pan-qiang

Funder

Youth Foundation of Project for Humanities

Social Sciences of Ministry of Education of China

Fund of Shanghai Scientific and Technological Development of Research Project for Soft Science

Publisher

Elsevier BV

Subject

Economics and Econometrics

Reference28 articles.

1. Maximum likelihood estimation of discretely sampled diffusions: a closed-form approximation approach;Aït-Sahalia;Econometrica,2002

2. Maximum likelihood estimators and random walks in long-memory models;Bertin;Statistics,2011

3. Large deviations inequalities for the maximum likelihood estimator and the Bayes estimators in nonlinear stochastic differential equations;Bishwal;Stat. Probab. Lett.,1999

4. Maximum likelihood estimation in partially observed stochastic differential system driven by a fractional Brownian motion;Bishwal;Stoch. Anal. Appl.,2003

5. Parameter estimation in stochastic differential equations;Bishwal,2008

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1. Berry–Esséen bound for the parameter estimation of fractional Ornstein–Uhlenbeck processes;Stochastics and Dynamics;2019-10-21

2. Parameter Estimation for Fractional Diffusion Process with Discrete Observations;Journal of Function Spaces;2019-01-08

3. Bayesian inference of the fractional Ornstein–Uhlenbeck process under a flow sampling scheme;Computational Statistics;2018-02-20

4. The rate of convergence of the Hurst index estimate for a stochastic differential equation;Nonlinear Analysis: Modelling and Control;2017-01-19

5. Parameter estimation of Gaussian stationary processes using the generalized method of moments;Electronic Journal of Statistics;2017-01-01

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